Learning Generalizable Patrolling Strategies through Domain Randomization of Attacker Behaviors
Carlos Diaz Alvarenga,Nicola Basilico,Stefano Carpin,Carlos Diaz Alvarenga,Nicola Basilico,Stefano Carpin
Graph-patrolling problems in the adversarial domain typically embed models and assumptions about how hostile events, from which an environment must be protected, are generated at a specific time and location. Relying upon such attacker models prevents algorithms from synthesizing strategies that can generalize in different settings, providing good performance under different and uncertain scenario...
